IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation
Large Language Models (LLMs) have significantly automated the process of scientific discovery over the past few years. However, existing systems share one core limitation: they generate and optimize ideas independently for either Quality or Diversity. This often leads to the generation of ideas in close proximity to one another or to a large set of trivial, unsound, or unclear concepts. In this work, we instead argue that research ideation should be treated as a conjunction of both objectives and framed as a Quality-Diversity (QD) search. In line with this perspective, we introduce IDEAgent, a multi-agent framework that manages the evolution of ideas through lineages. We jointly drive Qua...
| Authors | Varun Gumma, Navonil Majumder, Soumitra Sinhahajari et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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